B-cell acute lymphoblastic leukemia(B-ALL)is one of the most common pediatric malignancies,characterized by highly heterogeneous genetic alterations.The precise identification of high-risk genetic abnormalities,such as BCR-ABL1,KMT2A rearrangement,and TCF3-HLF,plays a crucial role in risk stratification,the formulation of chemotherapy regimens,and the selection of individualized therapies.High-risk cytogenetics can profoundly impact the trajectory of disease progression and therapeutic outcomes.Regarding therapy,the refinement of treatment strategies through precise molecular classification and risk stratification has catalyzed a gradual paradigm shift in clinical practice.This transition moves away from the traditional reliance on conventional chemotherapy agents toward individualized pharmacotherapy and disease management strategies based on prognostic risk stratification.In recent years,immunotherapy has achieved significant progress in leukemia treatment.Chimeric antigen receptor T-cell(CAR-T)therapy and monoclonal antibodies have emerged as novel therapies for patients with relapsed/refractory(R/R)B-ALL harboring high-risk cytogenetic features.These innovations have significantly improved the prognosis for this patient population.Currently,new clinical trials are continuously advancing.Emerging targeted therapies and cell therapies represented by CAR-T cells have become hot spots of current research and have demonstrated remarkable development potential.This review synthesizes recent therapeutic advances across pediatric B-cell acute lymphoblastic leukemia subtypes harboring high-risk genetic abnormalities,with a focused emphasis on the evolving role of immunotherapy.By critically integrating current evidence,we aim to outline rational strategies for developing safer and more effective therapies that can meaningfully improve clinical outcomes in these high-risk children and adolescents.

B-cell acute lymphoblastic leukemia(B-ALL)is one of the most common pediatric malignancies,characterized by highly heterogeneous genetic alterations.The precise identification of high-risk genetic abnormalities,such as BCR-ABL1,KMT2A rearrangement,and TCF3-HLF,plays a crucial role in risk stratification,the formulation of chemotherapy regimens,and the selection of individualized therapies.High-risk cytogenetics can profoundly impact the trajectory of disease progression and therapeutic outcomes.Regarding therapy,the refinement of treatment strategies through precise molecular classification and risk stratification has catalyzed a gradual paradigm shift in clinical practice.This transition moves away from the traditional reliance on conventional chemotherapy agents toward individualized pharmacotherapy and disease management strategies based on prognostic risk stratification.In recent years,immunotherapy has achieved significant progress in leukemia treatment.Chimeric antigen receptor T-cell(CAR-T)therapy and monoclonal antibodies have emerged as novel therapies for patients with relapsed/refractory(R/R)B-ALL harboring high-risk cytogenetic features.These innovations have significantly improved the prognosis for this patient population.Currently,new clinical trials are continuously advancing.Emerging targeted therapies and cell therapies represented by CAR-T cells have become hot spots of current research and have demonstrated remarkable development potential.This review synthesizes recent therapeutic advances across pediatric B-cell acute lymphoblastic leukemia subtypes harboring high-risk genetic abnormalities,with a focused emphasis on the evolving role of immunotherapy.By critically integrating current evidence,we aim to outline rational strategies for developing safer and more effective therapies that can meaningfully improve clinical outcomes in these high-risk children and adolescents.

Agricultural activities, including stock-farming, planting industry, and fish aquaculture, can affect the physicochemical and biological characters of freshwater lakes. However, the effects of pollution producing by agricultural activities on microbial ecosystem of lakes remain unclear. Hence, in this work, we selected Honghu Lake as a typical lake that is influenced by agriculture activities. We collected water and sediment samples from 18 sites, which span a wide range of areas from impacted and less-impacted areas. We performed a geospatial analysis on the composition of microbial communities associated with physicochemical properties and antibiotic pollution of samples. The co-occurrence networks of water and sediment were also built and analyzed. Our results showed that the microbial communities of impacted and less-impacted samples of water were largely driven by the concentrations of TN, TP, NO-N, and NO-N, while those of sediment were affected by the concentrations of Sed-OM and Sed-TN. Antibiotics have also played important roles in shaping these microbial communities: the concentrations of oxytetracycline and tetracycline clearly reflected the variance in taxonomic diversity and predicted functional diversity between impacted and less-impacted sites in water and sediment samples, respectively. Furthermore, for samples from both water and sediment, large differences of network topology structures between impacted and less-impacted were also observed. Our results provide compelling evidence that the microbial community can be used as a sentinel of eutrophication and antibiotics pollution risk associated with agricultural activity; and that proper monitoring of this environment is vital to maintain a sustainable environment in Honghu Lake.
Agriculture ; Animals ; Anti-Bacterial Agents ; analysis ; China ; Eutrophication ; Geologic Sediments ; chemistry ; microbiology ; Lakes ; chemistry ; microbiology ; Microbiota ; Risk Factors ; Water Pollutants, Chemical ; analysis

Radiotherapy is one of the main treatments for tumor with increasingly high request for technique precision and the equipment stability. Machine learning may bring radiotherapy simplicity, individualization and precision, and may improve the automatic level of planning and quality assurance. Based on the process of radiotherapy, this paper reviews the applications and researches on machine learning, with an emphasis on deep learning, and proposes the prospects in the following aspects: segmentation of normal tissue and tumor, planning, treatment delivery, quality assurance and prognosis prediction.
Deep Learning ; Humans ; Machine Learning ; Neoplasms ; radiotherapy

Objective To investigate the ultrasonographic manifestations of the wrestler's ear.Methods Fifteen healthy volunteers and 12 wrestlers and judokas underwent sonography on ears of both sides. Results The appearance of the wrestlei's ear was the thick auricle,wrestlers hydrocele,increasing of color Doppler signals and conchal cartilage injury. There was significant difference between the average auricular thickness of athletes[(0.95±0.41)cm]and that of volunteers[(0.41±0.07)cm,t=7.06,P＜0.(11].Conclusions The ultrasonographic appearance of the wrestler's ear is characteristic.It is significant of ultrasonography to be used in the evaluation of diagnosis and therapy.

Objective To investigate the effect of cilazapril on reactivit y of blood vessels in diabetic rats. 　Methods The mesenteric vessels of streptozotocin induced diabetic rats wer e perfused with Kreb-Ringer's solution in vitro,the mesenteric vessels reactivi ty of diabetic rats was measured by physiological recorder. 　Results The responsive continual time to norepinephrine was delay ed signific antly(9.4±3.1 vs 5.2±2.1,P<0.01) and the highest perfusion pressu re decrease significantly in diabetic rats(5.12±0.87 vs 7.81±0.92 kPa, P<0.01); When taken cilazapril showed no difference in these indexes in diabetic animal s.